Detection and recognition of foreign objects in Pu-erh Sun-dried green tea using an improved YOLOv8 based on deep learning. by Houqiao Wang, Xiaoxue Guo, Shihao Zhang, Gongming Li, Qiang Zhao, Zejun Wang

“…The method employs an MPDIoU optimized loss function to enhance target detection performance, thereby increasing the model's precision in targeting. …”
Get full text

Advancing precision agriculture: A comparative analysis of YOLOv8 for multi-class weed detection in cotton cultivation by Ameer Tamoor Khan, Signe Marie Jensen, Abdul Rehman Khan

“…The enhanced architecture of YOLOv8, featuring anchor-free detection, an advanced Feature Pyramid Network (FPN), and an optimized loss function, enables accurate detection even under challenging conditions. …”
Get full text

A Learning Framework of Nonparallel Hyperplanes Classifier by Zhi-Xia Yang, Yuan-Hai Shao, Yao-Lin Jiang

“…This framework not only includes twin SVM (TWSVM) and its many deformation versions but also extends them into multiclass classification problem when different parameters or loss functions are chosen. Concretely, we discuss the linear and nonlinear cases of the framework, in which we select the hinge loss function as example. …”
Get full text

Application Value Assessment of Grid Side Energy Storage Under Typical Scenarios in China by Bibin HUANG, Jing HU, Liping JIANG, Qionghui LI, Kaihui FENG, Bo YUAN

“…Based on the typical scenarios such as ensuring power system security, ensuring the function of power transmission and distribution, reducing the loss of power grid and improving the utilization level of new energy, a research is carried out on the grid side energy storage application value. …”
Get full text

UNet-Based End-to-End Anomaly Detection With Computational Hyperspectral Imaging by Weiming Shi, Junren Wen, Yipeng Chen, Yu Shao, Haiqi Gao, Xuehui Wang, Chenying Yang

“…The UNet is primarily designed for extracting spatial features, and the ELI, which embeds both spatial and implicit spectral information, makes it particularly well suited for leveraging UNet's feature extraction capabilities. Moreover, to optimize the tradeoff between anomaly detectability and background suppression, we incorporate a cost matrix into the loss function to adjust the emphasis on anomalies and the background. …”
Get full text

YOLO-SAATD: An efficient SAR airport and aircraft target detector by Daobin Ma, Zhanhong Lu, Zixuan Dai, Yangyue Wei, Li Yang, Haimiao Hu, Wenqiao Zhang, Dongping Zhang

“…Fine granularity: A ”ScaleNimble Neck” integrates feature reshaping and scale-aware aggregation to enhance detail detection and feature capture in multi-scale SAR images. 3. Precision: Wise-IoU loss function is used to optimize bounding box localization and enhance detection accuracy. …”
Get full text

Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets by Retinderdeep Singh, Sheifali Gupta, Ashraf Osman Ibrahim, Lubna A. Gabralla, Salil Bharany, Ateeq Ur Rehman, Seada Hussen

“…To address class imbalance and improve model generalization, a customized weighted cross-entropy loss function is incorporated. The model obtains improved interpretability through explainabile artificial intelligence (XAI) techniques, including Grad-CAM, SHAP, SmoothGrad, and LIME, providing deeper insights into prediction rationale. …”
Get full text

Early Intervention with a Preformed Eruption Guidance Appliance After the Excision of Cemento-Ossifying Fibroma: A Case Report by Yeonjin Ju, Soyoung Park, Jonghyun Shin, Taesung Jeong, Eungyung Lee

“…After surgical excision, an EGA was applied to facilitate proper eruption of the successor teeth and optimize oral muscle function. Over a four-year follow-up period, the permanent teeth in the affected area erupted favorably, and intercuspal relationships improved during the transition from primary to mixed dentition. …”
Get full text

PoulTrans: a transformer-based model for accurate poultry condition assessment by Jun Li, Bing Yang, Junyang Chen, Jiaxin Liu, Felix Kwame Amevor, Guanyu Chen, Buyuan Zhang, Xiaoling Zhao

“…This model incorporates visual features extracted by CNNs into the Channel Spatial Attention Segmentation Encoder (CSA_Encoder), which produces segmented channel and spatial attention outputs. To optimize multi-level attention and improve the semantic precision of the status descriptions, we introduced a Channel Spatial Memory-Guided Transformer (CSMT) and a novel PS-Loss function. …”
Get full text

Learning From Natural Images in Few-Shot SAR Target Classification by Songhao Shi, Xiaodan Wang, Yafei Song

“…In addition, we employ a Brownian distance covariance metrics module to overcome the limitations of conventional metric distances, ensuring the model acquires more precise semantic representations. A novel joint loss function is formulated to optimize cross-domain generalization and accommodate the unique imaging characteristics of SAR. …”
Get full text

Grape Target Detection Method in Orchard Environment Based on Improved YOLOv7 by Fuchun Sun, Qiurong Lv, Yuechao Bian, Renwei He, Dong Lv, Leina Gao, Haorong Wu, Xiaoxiao Li

“…Finally, the Focal-EIoU loss function is employed to optimize the regression prediction boxes, accelerating their convergence and improving regression accuracy. …”
Get full text

Sodium-glucose transporter 2 inhibitors and cardiovascular-kidney-metabolic syndrome: a narrative review by Yuqing Wang, Yaqing Wang, Xiaojie He, Xiaodong Li

“…Furthermore, SGLT2i have shown potential therapeutic roles at various stages of CKM syndrome, including improving cardiac function, slowing CKD progression, promoting weight loss, and improving lipid profiles. …”
Get full text

Low resolution remote sensing object detection with fine grained enhancement and swin transformer by Zhijing Xu, Xin Wang, Kan Huang, Ren Chen

“…Finally, we incorporate the Shape-IoU loss function to optimize bounding box regression, significantly improving small target detection accuracy while maintaining computational efficiency. …”
Get full text

Dual-Ascent-Inspired Transformer for Compressed Sensing by Rui Lin, Yue Shen, Yu Chen

“…Second, DAT exhibits enhanced robustness to variations in initial learning rates, as evidenced by loss function analysis during training.…”
Get full text

Classification of Diabetic Retinopathy Based on Efficient Computational Modeling by Jiao Xue, Jianyu Wu, Yingxu Bian, Shiyan Zhang, Qinsheng Du

“…By modifying the cross-entropy loss function, we enhance the model’s attention to rare categories, especially in large-scale multi-category classification tasks. …”
Get full text

Efficient vision transformers with edge enhancement for robust small target detection in drone-based remote sensing by Xuguang Zhu, Zhizhao Zhang

“…Combined with an Inner Generalized Intersection-over-Union (Inner-GIoU) loss function to optimize bounding box geometric consistency, MLD-DETR achieves 36.7% AP50% and 14.5% APs on the VisDrone2019 dataset, outperforming the baseline RT-DETR by 3.2% and 1.8% in accuracy while achieving 20% parameter reduction and maintaining computational efficiency suitable for UAV platforms equipped with modern edge computing hardware. …”
Get full text

Multiscale construction of wave-absorbing carbon nanomaterials by Tong Wu, Song Bi, Hao Li, Ruihua Xing, Jun Yang, Xuanyu Liu, Zhuoxun Li

“…The mesoscopic level focuses on the modulation of the micro-nano multilevel structure of carbon absorbers, such as the in situ multilevel assembly of MXene, MOFs and heterogeneous continuous fibers at the mesoscopic scale, which is conducive to the enhancement of the absorber's conductivity and interfacial loss to enhance its wave-absorbing ability. The macroscopic level focuses on structure–function integrated design, such as 3D porous structures, sandwich honeycomb structures, and surface superstructures, which enable the materials to possess excellent mechanical properties along with good wave-absorbing properties. …”
Get full text

GDFC-YOLO: An Efficient Perception Detection Model for Precise Wheat Disease Recognition by Jiawei Qian, Chenxu Dai, Zhanlin Ji, Jinyun Liu

“…This method is based on YOLOv11 and encompasses three key aspects of improvement: (1) a newly designed Ghost Dynamic Feature Core (GDFC) in the backbone, which improves the efficiency of disease feature extraction and enhances the model’s ability to capture informative representations; (2) a redesigned neck structure, Disease-Focused Neck (DF-Neck), which further strengthens feature expressiveness, to improve multi-scale fusion and refine feature processing pipelines; and (3) the integration of the Powerful Intersection over Union v2 (PIoUv2) loss function to optimize the regression accuracy and convergence speed. …”
Get full text

Damage Detection Method for Road Ancillary Facilities Integrating Attention Mechanism by Shuang Yang, Huiqin Wang, Ke Wang, Nan Guo

“…Lastly, the WIoUv3 bounding box loss function is employed to optimize the regression performance of bounding boxes, ensuring higher localization accuracy under different scales and overlap conditions. …”
Get full text

TWEAK promotes peritoneal inflammation. by Ana Belen Sanz, Luiz Stark Aroeira, Teresa Bellon, Gloria del Peso, Jose Jimenez-Heffernan, Beatriz Santamaria, Maria Dolores Sanchez-Niño, Luis Miguel Blanco-Colio, Manuel Lopez-Cabrera, Marta Ruiz-Ortega, Jesus Egido, Rafael Selgas, Alberto Ortiz

“…Peritoneal dialysis (PD) is complicated by peritonitis episodes that cause loss of mesothelium and eventually sclerosing peritonitis. …”
Get full text